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User-associated contents play an increasingly important role in modern network applications. With growing deployments of edge servers, the capacity of content storage in edge clusters significantly increases, which provides great potential to satisfy content requests with much shorter latency. However, the large number of contents also causes the difficulty of searching contents on edge servers in different locations because indexing contents costs huge DRAM on each edge server. In this work, we explore the opportunity of efficiently indexing user-associated contents and propose a scalable content-sharing mechanism for edge servers, called EdgeCut, that significantly reduces content access latency by allowing many edge servers to share their cached contents. We design a compact and dynamic data structure called Ludo Locator that returns the IP address of the edge server that stores the requested user-associated content. We have implemented a prototype of EdgeCut in a real network environment running in a public geo-distributed cloud. The experiment results show that EdgeCut reduces content access latency by up to 50% and reduces cloud traffic by up to 50% compared to existing solutions. The memory cost is less than 50MB for 10 million mobile users. The simulations using real network latency data show EdgeCut’s advantages over existing solutions on a large scale.
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Retina: Analyzing 100 GbE Traffic on Commodity Hardware
As network speeds have increased to over 100 Gbps, operators and researchers have lost the ability to easily ask complex questions of reassembled and parsed network traffic. In this paper, we introduce Retina, a software framework that lets users analyze over 100 Gbps of real-world traffic on a single server with no specialized hardware. Retina supports running arbitrary user-defined analysis functions on a wide variety of extensible data representations ranging from raw packets to parsed application-layer handshakes. We introduce a novel filtering mechanism and subscription interface to safely and efficiently process high-speed traffic. Under the hood, Retina implements an efficient data pipeline that strategically discards unneeded traffic and defers expensive processing operations to pre- serve computation for complex analyses. We present the framework architecture, evaluate its performance on production traffic, and explore several applications. Our experiments show that Retina is capable of running sophisticated analyses at over 100 Gbps on a single commodity server and can support 5–100x higher traffic rates than existing solutions, dramatically reducing the effort to complete investigations on real-world networks.
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- PAR ID:
- 10346556
- Date Published:
- Journal Name:
- ACM SIGCOMM 2022 Conference (SIGCOMM ’22)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3544216.3544227
